ChefBoyRD - The premier restaurant development platform - RU Software Eng. S2017
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Rutgers Software Engineering 2017

This is the repository for our Software Engineering course project, ChefBoyRD, which was completed during the spring semester of 2017.

Build Status Python 3.3+ GitHub license

Team Members:

  • Richard Ahn
  • Zachary Blanco
  • Benjamin Chen
  • Jeffrey Huang
  • Jarod Morin
  • Seo Bo Shim
  • Brandon Smith

Repository Structure

  • docs/ houses the code documentation for the project
  • chefboyrd/ houses all of the source code for the project
  • reports/ contains all of the submitted reports for the project.

View the README in each folder for more information about what is contain within each directory.

Developer Quick-Start

All commands below should work in the bash command line with a Debian distribution of GNU/Linux.

Step 1: Install Dependencies

  • For MacOS users you can install homebrew and replace all sudo apt install commands with brew install
  • For Windows users - good luck trying to install everything.

See the end of step one for a block of commands to copy-paste for setup.


  • python 3.3+ (sudo apt install python3)
  • python's package manager, pip (sudo apt install python3-pip)
  • NumPy (sudo apt install python3-numpy)
  • SciPy (sudo apt install python3-scipy)

Below is a command which will install everything (Copy-Paste)

sudo apt install python3 python3-pip python3-numpy python3-scipy

Once you've run that you need to clone this repository

git clone

cd into the directory

cd ChefBoyRD/

Great! Make sure that all of the code is there as expected. We'll need to use virtualenv to install our packages locally for our project.

sudo pip install virtualenv
virtualenv -p python3 env
source env/bin/activate

Okay, so now your prompt should look something like:

(env) zac@ZB-XPS13:~/.../chefboyrd$

Finally we just install our python packages

pip install -r requirements.txt

That will install the required python package dependencies so that we can import them successfully.

Here's all of the commands together.

sudo apt install python3 python3-pip python3-numpy python3-scipy
git clone
cd ChefBoyRD/
sudo pip install virtualenv
virtualenv -p python3 env
source env/bin/activate
pip install -r requirements.txt

Step 2: Ensure Unit Tests are Running

So now that we have all of the code and dependencies installed we can try to run the unit tests. While in the root directory of the repository run the following command:

    make test

You should see something like the following

    zac:ChefBoyRD$ make test
    python3 -m unittest discover -s chefboyrd/tests/
    Ran 3 tests in 0.001s


If you see any failures then you should investigate the issue. If you think there is an error or missing dependency which needs to be installed notify the repository maintainers

To under tests individually, specify the path to the desired test file. Here is an example:

python3 -m unittest chefboyrd/tests/

Running the Debug Server

Simply run the command

    make debug

This will start the debugging server at http://localhost:5000 where you can navigate to app web pages to test the server. You should see an output like the following.

zac:ChefBoyRD$ make debug
bash ./
  * Serving Flask app "chefboyrd"
  * Forcing debug mode on
  * Running on (Press CTRL+C to quit)
  * Restarting with stat
  * Debugger is active!
  * Debugger pin code: 369-149-264

The Amazon Alexa Skill

In order to run the Amazon Alexa skill you'll need to

  • Install ngrok
  • Create an AWS developer account

Once you have both you'll need to create a new Amazon Alexa skill that utilizes the same skill schema found under chefboyrd/alexa/skill.json. Information on how to create an Alexa skill can be found here (start from step 2)

When creating the skill select https endpoint rather than a lambda function. Leave this screen up.

Open up your bash terminal to the ChefBoyRD repository. Follow the instructions to install all dependencies.

cd chefboyrd
virutalenv -p python3 env
source env/bin/activate
pip install -r requirements.txt

After installing dependencies you can run


Once the skill is running you'll need to open a 2nd terminal and type ngrok http 5000. This will open a localhost tunnel to HTTP 5000 on your computer and give you an https endpoint. Copy the https url from ngrok and paste it into the https endpoint on your Alexa skill configuration. Use your echo or go to in order to test the chefboyrd skill.


Task Due Date
Proposal January 30th
Report 1 Part 1: Statement of Work & Requirements February 5th
Report 1 Part 2: Functional Requirements Spec & UI February 12th
Report 1: Full February 19th
Report 2 Part 1: Interaction Diagrams February 26th
Report 2 Part 2: Class Diagrams and System Architecture March 5th
Report 2: Full March 12th
First Demo March 27th
Report 3: Part 1: April 23rd
Second Demo April 25th


This restaurant automation application, ChefBoyRD, (ChefBoy Restaurant Development) is a software program that offers solutions to the specific problems. These problems include, improving business efficiency by reducing food waste, improviding feedback submissions and processing, and improving the customer experience with a reservation system. The relevant results generated by the program will be accessible by employees, e.g. chefs will have access to dish prediction, hosts and hostesses will see reservations. All of the program's functions can be centrally monitored by the manager.

Included Files and Description

Root directory

  • .gitignore - ignores files for git repository
  • .travis.yml - continuous integration configuration
  • config.ini - configuration file for DB name
  • - script to run the debug version of the application server
  • Makefile - run make targets in order to debug or run tests (with simpler commands)
    • make debug - runs the debug application server
    • make test - runs the unit and integration tests
  • requirements.txt - the list of python dependencies that are installed using pip
  • - The Amazon Alexa skill interface
  • templates.yaml - The text-audio templates for Alexa responses
  • using_twilio.txt - instructions on how to use/setup the twilio account for the application


  • skill.json - The JSON configuration for the Alexa skill (copy-paste into Alexa configuration on amazon site)


  • booking - Controller containing logic for table reservations and moving reservation data in/out of the database
  • criteriaLists.ini - configuration file containing lists of words to categorize different types of feedback
  • - functions for taking care of customer models - more or less used as a sample module that we based other modules off of
  • - Controller which is used to retrieve anything to do with restaurant order statistics and tabs. Houses business logic for retrieving order data from the database for the stat dashboard and prediction models
  • - controller containing business logic for customer feedback processing.
  • - controller containing logic to train our prediction model
  • - contains logic to use regression model with trained parameters to make predictions
  • - Interface containing functions to generate receipt information
  • - controller containing functions and logic to handle schedule, posting, and claiming work shifts.
  • sms.cfg - Twilio configuration parameters.


  • - The model class which all other models should inherit from
  • - The customers table model
  • - The rating table model
  • - the reservation table model
  • - Shift table model
  • - SMS feedback table model
  • - Contains the Tabs, Orders, Meals, MealIngredients, Quantities, and Orders table model definitions
  • - The application user model.


Static web application assets. Contains javascript, css, and images for our application. This content was not originally authored by our group members.


  • criteriaLists.ini - criteriaLists for testing feedback categorization
  • - Tests for using a sample http client to make auth requests
  • - creates data for testing feedback
  • - Tests the feedback module
  • - Tests authorization and authentication of the application
  • - Tests the basic application functionality for loading pages and known endpoints to make sure the application is generally functional.
  • - Tests the prediction and statistics dashboard controllers and views in order to ensure they work properly.
  • - tests the reservation module controller and views in order to ensure proper operation


HTML Jinja2 templates that are rendered together to form application pages

  • Each folder corresponds to a tab in our user interface
  • dashboard/ - statistics dashboard
  • feedbackC and feedbackM correspond to Customer and Management feedback views respectively.
  • settings is for user account creation through the UI
  • default.html - the template that all pages inherit from
  • footer.html - footer used for all pages
  • header.html - page header for all pages
  • head.html - the element used on all pages
  • login.html - the login page
  • unauthorized.html - the page displayed on HTTP 401 responses
  • _formhelpers.html - templates used to help display forms


  • feedback*.py - The flask blueprint endpoints for customer feedback
  • - The flask blueprint endpoint for the prediction tab
  • reservation*.py - The flask blueprint endpoints for table reservation
  • - A simple flask blueprint endpoint for our application. Used for tests.
  • - Flask blueprint endpoint for creating new users
  • - Flask blueprint endpoints for shift management
  • - Flask blueprint and endpoints for the statistics dashboard
  • - Flask blueprint endpoints for the table management interface.


  • - The file which sets up and connects the models, views, and controllers of the application in order to run the server. Generates sample data if necessary. Used when importing.
  • - Functions for user authentication and authorization.